A component content management system (CCMS) is a content management system (CMS) that manages content at a granular level (component) rather than at the document level. Each component represents a single topic, concept or asset (for example an image, table, product description, a procedure). The CCMS must be able to track “not only versions of topics and graphics but relationships among topics, graphics, maps, publications, and deliverables.
See this Gilbane Group white paper for a more detailed description Component Content Management – How True CCM Technology Drives the Most Compelling Content Initiatives.
The Semantic Web is a collaborative movement led by the international standards body, the World Wide Web Consortium (W3C). The standard promotes common data formats on the World Wide Web. By encouraging the inclusion of semantic content in web pages, the Semantic Web aims at converting the current web dominated by unstructured and semi-structured documents into a “web of data”. The Semantic Web stack builds on the W3C’s Resource Description Framework (RDF).
Also see linked data and knowledge graphs.
This Scientific American feature article from May 2001 sets out the vision (and yes, this is also a fun example of what a staid web page looked like in 2001):
The Semantic Web
A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities
by Tim Berners-Lee, James Handler and Ora Lasilla
Also see:
Introduction to Semantic Technology
and for a slightly more skeptical point of view:
Web 2.0, 3.0 and so on
and this:
Why Adding Semantics to Web Data is Difficult
In computer science and information science, an ontology is a formal naming and definition of the types, properties, and interrelationships of the entities that really or fundamentally exist for a particular domain of discourse. It is thus a practical application of philosophical ontology, with a taxonomy. An ontology compartmentalizes the variables needed for some set of computations and establishes the relationships between them. The fields of artificial intelligence, the Semantic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture all create ontologies to limit complexity and to organize information. The ontology can then be applied to problem solving.
Content accounting: calculating value of content in the enterprise
Sarah O’Keefe provides a guide for measuring the business value of content for companies of all sizes. Helpful for content professionals, project managers, and senior management. Includes a sample P&L and balance sheet. Justify your project. Read More
Content management on intranets: centralized, distributed, and hybrid models
This will be basic for many of you but is a clear and accessible description of the differences and the pros and cons of each model to share with non-specialist or non-technical colleagues. Read More
Google vs EU pubs and Facebook’s new trick
Frederic Filloux looks at the state of the complicated dance among EU publishers, Google, and Facebook in light of the recent announcements and motivations of each of them, and some research on news search behavior. A good read. Read More
The key to millions: enterprise search?
Steve Arnold dishes out a dose of reality in his inimitable slightly snarky way on the realities of the enterprise search market. Read More
Also…
The Gilbane Advisor curates content for content, computing, and digital experience professionals. We focus on strategic technologies. We publish more or less twice a month except for August and December.
Knowledge management (KM) comprises a range of strategies and practices used in an organization to identify, create, represent, distribute, and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organizations as processes or practices.
See also:
The Gilbane Report: Vol 12, Num 9 — KM as a Framework for Managing Knowledge Assets
Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and machine learning or artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.
A document management system (DMS) is a computer system (or set of computer programs) used to track and store electronic documents in various forms, including:
- marked-up text files for editing and publishing to printing or electronic display devices, using either proprietary or standard markup languages
- “final form” print-oriented page languages such as PostScript or PDF
- rasterized images that have been scanned for archival or viewing
Basic features included check-in / check-out library services for authors, and version tracking.
Since the emergence of the Web and multichannel content management systems (CMS) in the 1990s and since, document management systems have largely become a subset of the broader content management category.
“Information technology” (IT) likely first appeared in a Harvard Business Review article in November 1958, and refers to the use of computing technology to create, process, manage, store, retrieve, share, and distribute information (data).
Early use of the term did not discriminate between types of information or data, but in practice, until the late 1970s, business applications were limited to structured data that could be managed by information systems based on hierarchical and then relational databases. Also see content technology and unstructured data.
